Objective-space discretization is a popular method to control the elitist archive size for evolutionary multi-objective optimization and avoid problems with convergence. By setting the level of discretization, the proximity and diversity of the Pareto approximation set can be controlled. This paper proposes an adaptive archiving strategy which is developed from a rigid-grid discretization mechanism. The main advantage of this strategy is that the practitioner just decides the desirable target size for the elitist archive while all the maintenance details are automatically handled. We compare the adaptive and rigid archiving strategies on the basis of a performance indicator that measures front quality, success rate, and running time. Experimental results confirm the competitiveness of the adaptive method while showing its advantages in terms of transparency and ease of use.
Additional Metadata
Keywords Multiobjective optimization, estimation of distribution algorithms, elitist archive
THEME Software (theme 1), Logistics (theme 3), Energy (theme 4)
Publisher Springer
Project Computational Capacity Planning in Electricity Networks
Conference International Conference on Parallel Problem Solving from Nature
Citation
Luong, N.H, & Bosman, P.A.N. (2012). Elitist archiving for multi-objective evolutionary algorithms: To adapt or not to adapt. In Proceedings of International Conference on Parallel Problem Solving from Nature 2012 (pp. 72–81). Springer.